A system and method provide the ability to control an architecture, engineering, and construction (AEC) project workflow. AEC data regarding a quality of construction is obtained. A set of classifiers and machine learning models are obtained. The AEC data is augmented based on the set of classifiers and machine learning models. A risk metric is generated for one or more issues in the AEC data based on the augmented AEC data. The risk metric is interactively generated and presented on a display device. Work, project resourcing, and/or training are prioritized based on the risk metric.
Legal claims defining the scope of protection, as filed with the USPTO.
2. The computer-implemented method of claim 1, wherein the risk level is classified as high, medium, or low.
3. The computer-implemented method of claim 1, wherein the risk category is selected from a group consisting of Water, Rework, Inspection, and High Value.
4. The computer-implemented method of claim 1, wherein the issue risk classification machine learning model is developed based on augmented labelled data and a feedback loop that accepts user input that is used to update the issue risk classification machine learning model.
5. The computer-implemented method of claim 1, wherein the subcontractor machine learning model predicts the subcontractor risk metric based on project data and ranks subcontractors based on which subcontractor needs the most attention on a specific day.
7. The computer-implemented method of claim 1, wherein the visualization comprises a risk heat map view of subcontractor risk levels over time, wherein the heatmap is used to further compare and hire subcontractors.
8. The computer-implemented method of claim 1, wherein the subcontractor risk metric comprises a label of a risk-level tag that is representative of an amount of work the subcontractor is currently accountable for and a track record of the subcontractor on a project.
11. The computer-implemented system of claim 10, wherein the risk level is classified as high, medium, or low.
12. The computer-implemented system of claim 10, wherein the risk category is selected from a group consisting of Water, Rework, Inspection, and High Value.
13. The computer-implemented system of claim 10, wherein the issue risk classification machine learning model is developed based on augmented labelled data and a feedback loop that accepts user input that is used to update the issue risk classification machine learning model.
14. The computer-implemented system of claim 10, wherein the subcontractor machine learning model predicts the subcontractor risk metric based on project data and ranks subcontractors based on which subcontractor needs the most attention on a specific day.
16. The computer-implemented system of claim 10, wherein the visualization comprises a risk heat map view of subcontractor risk levels over time, wherein the heatmap is used to further compare and hire subcontractors.
17. The computer-implemented system of claim 10, wherein the subcontractor risk metric comprises a label of a risk-level tag that is representative of an amount of work the subcontractor is currently accountable for and a track record of the subcontractor on a project.
Cooperative Patent Classification codes for this invention. Click any code to explore related patents in that topic.
November 24, 2020
May 30, 2023
Browse 5M+ US patents with plain-English claim translations and AI-generated analysis.